David L Tabb, Ph.D.

Associate Professor of Biomedical Informatics
Associate Professor of Biochemistry
U9211 Learned Lab / MRB III
465 21st Ave S
Phone Number: 
(615) 936-0380
Fax Number: 
(615) 343-8372
Email Address: 

David Tabb was named one of two White House Presidential Scholars for Missouri in 1992. He attended college at the University of Arkansas as a Sturgis Fellow. He majored in Biology and minored in Computer Science, graduating summa cum laude in 1996. His honors thesis described the design of software for the analysis of genetic sequences.

He carried this interest in bioinformatics to graduate school at the University of Washington's Molecular Biotechnology department. There he studied proteomics as a graduate student under John Yates. The laboratory moved to The Scripps Research Institute in 2000. David worked closely with Vicki Wysocki’s group at the University of Arizona in characterizing peptide fragmentation during low-energy Collision Induced Dissociation through a series of key papers. David created software for data mining proteomic results to extract biological information more consistently and rapidly; "DTASelect" has become one of the most widely used software tools in proteomics. He leveraged his understanding of peptide fragmentation to create the first fully-automated sequence tag infrastructure in the "GutenTag" software package.

In 2003, David completed his Ph.D. and began a post-doctoral fellowship at Oak Ridge National Laboratory. While there, he developed "DBDigger," a novel database search algorithm that streamlined the process of proteomic identification. He focused on reducing the redundancy of these data sets through the creation of "MS2Grouper," a tool that employs graph theory to examine spectral inter-relationships. Dr. Tabb also examined the use of high-resolution mass spectra for the purpose of peptide charge state inference.

In 2005, David joined the faculty of Vanderbilt University Medical Center to lead a group in the Mass Spectrometry Research Center. In 2006, he was appointed as an assistant professor in the Biochemistry Department as well. His work focuses on identifying peptides more successfully from clinical samples, specifically peptides that have mutated or modified peptide sequences.


  • The core competency of the Tabb Laboratory is proteomic identification from MS/MS data. The team has contributed to three complementary technologies for peptide identification: spectral libraries, sequence tagging, and conventional sequence database searching. The team develops the scalable IDPicker framework to filter the identifications from these tools, to organize identifications for hundreds or thousands of experiments, and to build minimal protein sets to explain observed peptides. The laboratory conducts research to model the process by which peptides fragment in MS/MS in order to improve identification.  Other research improves techniques for comparative proteomics, especially in the area of cancer biomarkers.  All tools from the laboratory are available with open source.
  • As systems biotechnology advances, the complexity of individual experiments has grown substantially.  Conducting reproducible experiments requires careful control of many potential sources of variability.  The Tabb Laboratory conducts research to generate quality metrics from experimental data and to make decisions about instrument performance suitability using its QuaMeter framework.
  • As the Tabb Laboratory grows, two key areas of expansion are planned.  The first seeks to tie RNA-Seq transcriptomic expression to proteomic expression; the Laboratory has begun mapping the gene-protein relationships for integrating these data sets.  The second area of research seeks to determine quantities and identities from metabolomic data sets, so that protein expression and enzymatic pathways can be evaluated.